wireless communications in the era of energism · 2015-07-10 · 3 wireless communication powered...

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Wireless Communications in the Era of Energism

Rui Zhang (e-mail: [email protected])

ECE Department, National University of Singapore (NUS)

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Acknowledgement

Rui Zhang, National University of Singapore

Collaborators Chin Keong Ho (I2R, Singapore) Shuguang Cui (Texas A&M U) Kaibin Huang (U Hong Kong) Lingjie Duan (SUTD) Teng Joon Lim (NUS) Kee Chaing Chua (NUS)

My research team at NUS

Post doctorate fellows Suzhi Bi, Jie Xu, Liang Liu, Hyungsik Ju, Yong Zeng, Gang Yang

PhD students Shixin Luo, Xun Zhou, Seunghyun Lee, Yinghao Guo, Yueling Che

Visiting PhD students Hong Xing (KCL), Chuan Huang (Texas A&M U)

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Wireless Communication Powered by Batteries (Conventional)

Introduction Rui Zhang, National University of Singapore

Need manual battery recharging/replacement Costly, inconvenient, abruption to use Inapplicable in some scenarios, e.g., implanted medical devices,

sensors built in cement structures Communication system design metric: energy saving

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Wireless Communication Powered by Energy Harvesting (More Recent) Introduction Rui Zhang, National University of Singapore

External energy source: solar, wind, vibration, ambient radio power, etc. Inexpensive, green, renewable However, environment energy is intermittent and unpredictable, uneven temporally

and geographically, low in power, costly/bulky energy harvesting device, etc. In practice, storage device often used to store excessive energy for future use Communication design paradigm shift: “energy saving” → “energy awareness”

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Wireless Communication Powered by Wireless Power Transfer (Emerging) Introduction Rui Zhang, National University of Singapore

Dedicated wireless power transmitter connected to fixed/renewable power source Wireless charging fully controllable in transmit power, frequency, waveform, etc. Charging and communicating simultaneously, no disruption to use Wide coverage, low production cost, and small receiver form factor Main challenges: efficient wireless power transfer, wireless powered communication

network (WPCN) design, simultaneous wireless information and power transfer (SWIPT)

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Cellular Networks Powered by Power Grid (Conventional)


Base station

Transmission grid Distribution grid Centralized generation

Cellular network

Power grids

One-way energy flow from power grid to base stations (BSs) Fixed energy pricing Energy cost minimization = total energy consumption minimization Techniques for energy saving: BSs on/off switching, cell zooming, load

balancing, energy-efficient transmission, etc.

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Cellular Networks with Renewable Energy Integration (More Recent)


Use renewable energy (solar/wind) when available to substitute conventional on-grid energy since lower cost (mainly deployment), more environmentally friendly

Practical challenges: intermittent, unpredictable, spatially and temporally varying Network operation needs re-design to be adaptable with “energy fluctuation” Energy cost minimization = “on-grid” energy consumption minimization

Base station


Solar panel Wind turbine

Cellular network

Power grids

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Cellular Networks Powered by Smart Grid (Emerging) Introduction Rui Zhang, National University of Singapore

Base station


Solar panel Two-way energy flow

Two-way information flow

Wind turbine

Cellular network

Smart meter

Distributed renewable generation

Smart meter Smart meter Power grids

Two-way energy/information flows between power grids and cellular network Possible energy sharing among BSs with individual harvested energy Two-way energy trading between power grids and cellular network Dynamic energy pricing and demand side management Distributed BSs can be formed as one or more microgrids Energy cost minimization ≠ energy consumption minimization

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Organization of this Talk


Energy-harvesting Communications

Wireless-powered Communications

SmartGrid-powered Communications

Staircase/directional water-filling

Energy harvesting cooperative

communication

Offline vs. Online energy control

Energy full/half duplex

Energy beamforming

Rate-energy tradeoff

Separated vs. Integrated receiver for

SWIPT

Joint communication and energy transfer scheduling & design

Two-way energy trading

Energy cost-aware resource allocation &

optimization

Joint energy/communication cooperation design

Energy cooperation

Energy diversity Doubly near-far problem

Hybrid powered base stations (BSs)

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Outlines


Energy-Harvesting Communications

Wireless-Powered Communications

SmartGrid-Powered Communications

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Example Applications

Energy-Harvesting Communications Rui Zhang, National University of Singapore

EH-link wireless sensor node that harvests energy from piezoelectric,

electrodynamic, and/or thermoelectric generators

AmbiMax wireless sensor node with a solar panel and a wind

generator

Low-power RFID tag that harvests energy from ambient

light or RF energy

Functional prototype of piezoelectric-powered RFID

shoes with mounted electronics

Cellular BS powered by hybrid solar and wind energy

Titan Aerospace’s solar-powered unmanned plane for delivering wireless internet access to developing nations

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A point-to-point communication link with an EH transmitter


Time-varying EH rates and wireless communication channels

EH rate

Channel gain

……

Time

Time

……

m-th EH block

(n,m)-th communication slot

Time

Energy EH profile

Feasible energy consumption region

EH constraints

Problem formulation for throughput maximization subject to EH constraints

Optimal solution: staircase (directional) water-filling power allocation

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Optimal “Offline” Power Allocation for Throughput Maximization


( ){ }( ) ( )( )

( ) ( ) ( ) ( )

2, 0 1 11 1

1 1 1 1

maximize log 1 , ,

subject to , , , ,

M N

P n m m nm N n m

j i i j

h n m P n m

P i j P i m N E j nE m n m

≥ = =

− −

= = = =

+

+ ≤ + ∀

∑∑

∑∑ ∑ ∑

Time

( )1 ,h n m

Time

( )1 ,h n mWater level

( ),P n m

Water level

( ),P n m

Conventional water-filling with sum-power constraint

Staircase water-filling with EH constraints

Exploiting a new form of energy diversity resulted from independent energy arrivals over time at the source and relay

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Energy Harvesting Cooperative Communications


Cooperative diversity order =2

Energy diversity order = 2

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Energy Half/full Duplex


Energy Half Duplex

Energy Full Duplex

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Energy Harvesting Wireless Communication Networks


“Energy heterogeneous” networks: time and spatially varying energy availability, different energy harvesting device/storage capacity over nodes, etc.

Joint “data queue” and “energy queue” management Throughput-optimal “on-line” (real-time) policy is in general unknown

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Selected Publications


Point-to-point EH communication for throughput maximization Staircase/directional water-filling power allocation: [HoZhang-TSP2012] [OzelTutuncuogluYangUlukusYener-JSAC2011]

Point-to-point EH communication for outage minimization: [HuangZhangCui-TWC2014]

Imperfect circuits and/or storage: [DevillersGunduz-JCN2012], [LuoZhangLim-TWC2013], [XuZhang-JSAC2014]

Online energy scheduling: [SharmaMukherjiJosephGupta-TWC2010], [BlascoGunduzDohler-TWC2013]

EH relay channels: [HuangZhangCui-JSAC2013], [LuoZhangLetaief-TWC2013], [GurakanOzelYangUlukus-TCOM2013]

EH communications under other setups Multiple-access channels: [YangUlukus-JCN2012] Broadcast channels: [YangOzelUlukus-TWC2012] Interference channels: [TutuncuogluYener-JCN2011]

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Future Directions


Different assumptions about the channel state information (CSI) and energy state information (ESI) at EH transmitters Optimal energy management policies under causal/non-causal CSI and

causal/non-causal ESI at transmitters

Case with EH receivers EH constraints are imposed at the EH receiver for signal decoding energy

consumption

Real-time design Jointly schedule the energy usage and data packet transmission by considering

the uncertainties in both energy and data arrivals

Hybrid energy sources and imperfect energy storage devices How to optimally design the energy management policies under practical

energy cost and storage considerations

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Recent Results


[1] H. Li, J. Xu, R. Zhang, and S. Cui, “A general utility optimization framework for energy harvesting based wireless communications,” IEEE Communications Magazine, vol. 53, no. 4, pp.79-85, April, 2015. [2] S. Ulukus, A. Yener, E. Erkip, O. Simeone, M. Zorzi, P. Grover, and K. Huang, “Energy harvesting wireless communications: a review of recent advances,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 3, pp. 360-381, March 2015.

[3] D. Gunduz, K. Stamatiou, N. Michelusi, and M. Zorzi, “Designing intelligent energy harvesting communication systems,” IEEE Communications Magazine, vol. 52, no. 1, pp. 210–16, January 2014.

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Outlines





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Wireless-Powered Communications: A General Model

Wireless-Powered Communications Rui Zhang, National University of Singapore

Wireless power transfer (WPT): deliver mW-level power using radio frequency (RF) signals from a range up to tens of meters

Three canonical operating modes Wireless power transfer: AP2 -> WD5; Wireless powered communication: AP1 <-> WD3, AP2->WD6->AP3; Simultaneous wireless information and power transfer: AP1->WD4, AP1->WD1/WD2

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Applications Wireless-Powered Communications Rui Zhang, National University of Singapore

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Historical Development of WPT


Nikola Tesla’s Wardenclyffe Project in early 1900’s

William C. Brown invented “rectenna” in 1960’s

Inductive coupling for RFID remote power supply in 1970’s

RF and magnetic resonant coupling enabled commercial devices from 2000’s

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Summary of Wireless Power Transfer Technologies


Strength Efficiency Distance Multicast Mobility Safety

Inductive Coupling Very high Very high Very short Yes No Yes

Magnetic Resonant Coupling

High High Short Difficult No Yes

EM Radiation

Omnidirectional Low Low Long Yes Yes Yes

Unidirectional (e.g. microwave,

laser)

High High Very long (LOS)

Difficult Difficult ?

Range

Efficiency

Inductive coupling Magnetic

resonant coupling

Electromagnetic (EM) radiation

<5cm pre-determined distance, e.g., 10-50cm

>1m

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WPT Transceiver Structure


The receiver uses rectifier to convert RF signal into DC signal Neglecting the noise power, the harvested power by the battery:

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Scaling Up WPT: Energy Beamforming in MIMO Channel


Antenna gain Path loss Energy conversion efficiency: 30% - 70%

The harvested energy is Q:What’s the optimal transmit strategy given a limited Tx power budget? A: Energy Beamforming (EB)

Optimize the transmit covariance matrix S The optimal EB is the principal eigenvector beamforming

s y

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MIMO Wireless Power Multicasting


Energy near-far problem: fairness is a key issue in the multicast EB design EB requires the accurate knowledge of channel state information at the transmitter (CSIT) Full CSIT is often absent, solutions:

Isotropic transmission (no CSIT needed, low efficiency) EB based on reverse-link training (energy constrained) EB based on statistical CSIT EB based on limited feedback (energy and hardware constrained)

CSI feedback

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Wireless Powered Communications Networks (WPCN)


In/out-band DL energy transmission (ET) and UL information transmissions (IT)

Performance tradeoff between DL (energy) vs. UL (information) transmissions How fast to transmit in UL IT related to the energy harvested from DL ET How much energy harvested in DL is also related to the UL channel

training/feedback Need joint energy and communication scheduling and resource allocation

Co-located/Separated ET transmitter and IT receiver

Co-located ET transmitter/ IT

receiver

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“Doubly” Near-far Problem

Doubly Near-Far Problem Due to distance-dependent signal attenuation in both DL and UL “Near” user harvests more energy in DL but transmits less power in UL “Far” user harvests less energy in DL but transmits more power in UL

Possible Solutions: Adaptive UL and UL transmission scheduling (TDMA) Joint DL EB and UL power control (SDMA) User cooperation (near user helps relay far user's message)

Harvest-then-transmit protocol

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Simultaneous Wireless Information and Power Transfer (SWIPT)

Wireless Power Transfer vs. Wireless Information Transfer Power Transfer : Energy (in Joule) is linearly

proportional to both time and power Information Transfer :

Information quantity (in bits) increases linearly with time but logarithmically with power

Optimal transmit power allocation over frequency-selective channel

Maximize data rate Maximize energy transfer

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Rate-Energy Tradeoff for an Ideal EH/ID Receiver

An “ideal” receiver can harvest the energy and decode information simultaneously However, practical receiver cannot achieve both from the same signal

Rate-energy region: all the achievable rate and energy pairs under a given transmit

power constraint P

Example: Rate-energy tradeoff in a SISO AWGN channel

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Practical SWIPT Receiver Structures

Time switching receiver Power splitting receiver

Integrated EH/ID receiver

Antenna switching receiver

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Rate-Energy Regions of SWIPT in Point-to-Point AWGN

0 1 2 3 4 5 60

10

20

30

40

50

60

Rate(bits/channel use)

Ene

rgy

Uni

t

Ideal RxPower Splitting RxTime Switching RxIntegrated Rx

?

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Dual Role of Interference in SWIPT

Interference is harmful to information decoding but useful to energy harvesting Opportunistic EH and ID in fading channel

Decode information when SNR is sufficiently high and received signal (information + interference) is weak

Harvest energy otherwise

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Section Summary

WPCN (Co-located ET transmitter/IT receiver)

Energy transfer Information transfer

SWIPT (Co-located EH/ID receivers)

SWIPT (Separated EH/ID receivers)

WPCN (Separated ET transmitter/IT receiver)

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Selected Publications Coding and information theory for ideal SWIPT receiver: [Varshney-ISIT2008],[GroverSahai-

ISIT2010], [PopovskiFouladgarSimeone-TCOM2013]

Practical SWIPT receivers and rate-energy tradeoff : [ZhangHo-TWC2013],[ZhouZhangHo-TCOM2013]

MIMO transmit optimization and resource allocation in multiuser SWIPT Broadcast channel: [XuLiuZhang-TSP2014], [HuangLarsson-TSP2014], [NgLoSchober-TWC2013] Relay channel: [KrikidisTimotheouSasaki-CommLett2012],[NasirZhouDurraniKennedy-TWC2013] Interference channel: [ParkClerckx-TWC2013],[LeeLiuZhang-TWC2015]

Joint energy and communication optimization in WPCN: [JuZhang-TWC2014], [HuangLau-

TWC2014], [JuZhang-TCOM2014], [LiuZhangChua-TCOM2014]

Wireless power transfer under imperfect CSIT: [XuZhang-TSP2014], [ZengZhang-TCOM2015]

Other topics Secrecy communications in SWIPT: [LiuZhangChua-TSP2014], [NgLoSchober-TWC2014] RF powered cognitive radio networks : [LeeZhangHuang-TWC2013] Wireless information and power transfer coexisting : [XuBiZhang-CommLett2015]

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Future Directions

Information-theoretic limits and coding for WPCN/SWIPT

Massive MIMO based WPT/WPCN/SWIPT

Imperfect CSIT and practical feedback in WPT/WPCN/SWIPT

Full-duplex WPCN/SWIPT

New transceiver design for WPCN/SWIPT

Many others (hardware development, safety/security/economic issues, MAC/network layer design, …….)

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Recent Results

[1] S. Bi, C. K. Ho, and R. Zhang, “Wireless powered communication: opportunities and challenges,” IEEE Communications Magazine, vol. 53, no. 4, pp. 117-125, April 2015. [2] X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “Wireless Networks With RF Energy Harvesting: A Contemporary Survey,” IEEE Communications Surveys and Tutorials, vol. 17, no. 2, pp.757-789, Second quarter 2015. [3] I. Krikidis, S. Timotheou, S. Nikolaou, G. Zheng, D. W. K. Ng, and R. Schober, “Simultaneous wireless information and power transfer in modern communication systems”, IEEE Communications Magazine , vol. 52, no. 11, pp. 104-110, Nov. 2014.

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Outlines





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Smart Grid Powered Cellular Networks: A Generic Model

SmartGrid-Powered Communications Rui Zhang, National University of Singapore

Base station


Solar panel Two-way energy flow

Two-way information flow

Wind turbine

Cellular network

Smart meter

Distributed renewable generation

Smart meter Smart meter Power grids

Hybrid energy supply at cellular BSs Renewable energy: cheap but intermittent (stochastically and spatially distributed) On-grid power: reliable but expensive

How to maximally utilize the cheap renewable energy to minimize the on-grid energy cost?

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Renewable Energy Supply and Traffic Demand Dynamics


Spatially distributed harvested energy Spatially distributed traffic load

Cellular network with renewable powered BSs

Net load (power consumption offset by harvested renewable energy) at BSs

Some BSs are renewable energy adequate Others are renewable energy deficit Both spatially and temporarily varying

How to make more balanced use of renewable energy to reduce total energy cost?

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Key Approaches


Approach I: Energy Cooperation on Supply Side Only BSs exploit two-way energy trading in

smart grid for energy sharing

Approach II: Communication Cooperation on Demand Side Only BSs share wireless resources and reshape

wireless loads to match individual renewable energy supplies

Approach III: Joint Energy and Communication Cooperation on Both Supply an Demand Sides

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Energy Cooperation


With energy cooperation

BS 1 BS 2

BS 1 BS 2

Aggregator

Without energy cooperation

BS 1 with excessive renewable energy (E1>Q1) shares energy to BS 2 in energy shortage (E2<Q2) via energy trading with the Aggregator

E1 decreases, and E2 increases

Total on-grid energy cost of two BSs is minimized

Spectrum for BS 1

Spectrum for BS 2

Spectrum for BS 1

Spectrum for BS 2

Spectrum for BS 1 Spectrum for BS 2

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Energy Cost-aware Communication Cooperation


With spectrum sharing

Spectrum for BS 1

BS 1 BS 2

Spectrum for BS 2

BS 1 BS 2

Without spectrum sharing

BS 1 with excessive renewable energy (E1>Q1) shares spectrum to BS 2 in energy shortage (E2<Q2)

Q1 increases, and Q2 decreases; hence, total on-grid energy cost is minimized

Other communication cooperation methods: traffic offloading, CoMP, etc.

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Joint Energy and Communication Cooperation


With joint energy and communication cooperation

Spectrum for BS 1

BS 1 BS 2

Spectrum for BS 2

BS 1 BS 2

Joint energy and communication cooperation BSs jointly optimize energy sharing and communication cooperation (e.g.,

spectrum sharing, traffic offloading, and CoMP) Achieve maximum energy cost reduction

Spectrum for BS 1 Spectrum for BS 2

Aggregator

Without energy/communication cooperation

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Case Study


Schemes for comparison Conventional design without energy or communication cooperation Approach I: energy cooperation via energy trading Approach II: communication cooperation via spectrum sharing Approach III: joint energy trading and CoMP

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BS1’s renewable energy supply

BS2’s renewable energy supply

BS1’s renewable energy consumption

BS2’s renewable energy consumption

Total energy cost

Conventional design w/o energy or communication cooperation

10

2.5

4.14

18.28

15.78

Energy cooperation via energy trading

4.14

8.36

4.14

18.28

10.51

Communication cooperation via spectrum sharing

10

2.5

10

14.04

11.54

Joint energy trading and CoMP

6.87

5.62

6.87

5.77

0.46

Renewable energy supplies are modified via energy cooperation

Energy demands are rescheduled via communication cooperation

Joint energy and communication cooperation saves the most energy cost

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Selected Publications


Energy cooperation in smart grids: [SaadHanPoorBasar-SPM2012]

Energy cooperation in cellular networks: [ChiaSunZhang-TWC2014]

Joint energy and communication cooperation in cellular networks Energy sharing/trading with CoMP: [XuZhang-TVT2015a], [XuZhang-TVT2015b] Joint energy and spectrum cooperation: [GuoXuDuanZhang-TCOM2014]

Energy management in cellular networks with renewable supplies: [HanAnsari-TWC2013], [ZhengPawelczakStanczakYu-CL2013], [GongThompsonZhouNiu-TCOM2014]

Joint management of cellular networks and smart grids: [BuYuCaiLiu-TWC2012], [HongZhu-SmartGridCom2014]

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Future Directions


Multi-time-scale implementation of joint energy and communication cooperation under practical constraints Energy harvesting rates in general change much slowly as compared to wireless channel

and traffic load variations

Cooperation between self-interested system operators with incomplete and private information sharing constraints Incentive mechanisms design to motivate different systems to cooperate with "win-win"

and fair cost reductions

Energy and communication cooperation in heterogeneous networks Need to address both heterogeneous communication demands and heterogeneous

energy supplies

Joint spatial and temporal energy/communication cooperation with energy storage management Exploiting both time and space energy diversity

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Recent Results


[1] J. Xu, L. Duan, and R. Zhang, “Cost-aware green cellular networks with energy and communication cooperation,” IEEE Communications Magazine, vol. 53, no. 5, pp. 257-263, May 2015. [2] T. Han and N. Ansari, “Powering mobile networks with green energy,” IEEE Wireless Communications, vol. 21, no. 1, pp. 90-96, February 2014.

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Conclusions


Energy-harvesting Communications

Wireless-powered communications

SmartGrid-powered communications

Staircase/directional water-filling

Energy harvesting cooperative

communication

Offline vs. Online energy control

Energy full/half duplex

Energy beamforming

Rate-energy tradeoff

Separated vs. Integrated receiver for

SWIPT

Joint communication and energy transfer scheduling & design

Two-way energy trading

Energy cost-aware resource allocation &

optimization

Joint energy/communication cooperation design

Energy cooperation

Energy diversity Doubly near-far problem

Hybrid powered base stations (BSs)

wireless communications in the era of energism · 2015-07-10 · 3 wireless communication powered...

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